Biomedical subjects

E J Engelken

Publications and source records attributed to E J Engelken.

At least 19 recordsLinked to original sources

PubMed1997

Effects of vestibular and oculomotor stimulation on responsiveness of the carotid-cardiac baroreflex.

Twelve healthy men underwent measurement of their carotid-cardiac baroreflex response during varying conditions of vestibulo-oculomotor stimulation to test the hypothesis that vestibular and/or oculomotor stimulation associated with head movements in the yaw plane inhibit baroreflex control of heart rate. We assessed the carotid-cardiac baroreflex response by plotting R-R intervals (in milliseconds) at each of eight neck pressure steps with their respective carotid distending pressures (in millimeters mercury). Baroreflex sensitivity was measured under four experimental conditions: 1) sinusoidal whole body yaw rotation of the subject in the dark without visual fixation (combined vestibular-oculomotor stimulation); 2) yaw oscillation of the subject while tracking a small head-fixed light moving with the subject (vestibular stimulation without eye movements); 3) subject stationary while fixating on a small light oscillating in yaw at the same frequency, peak acceleration, and velocity as the chair (eye movements without vestibular stimulation); and 4) subject stationary in the dark (no eye or head motion). Head motion alone reduced baseline baroreflex responsiveness by 30% from 3.8 +/- 0.5 to 2.6 +/- 0.5 ms/mmHg. Eye motion alone also reduced the baroreflex response by 13% (0.5 ms/mmHg) to 3.3 +/- 0.5 ms/mmHg. During head motion, the effect of eye motion was negligible (2.7 +/- 0.4 ms/mmHg). These results suggest that vestibular stimulation associated with head movements in yaw inhibits vagally mediated baroreflex control of heart rate, whereas oculomotor stimulation is less of a factor and only in the absence of vestibular stimulation.

Adult↗

PubMed1996

Effects of cerebellar lesions on saccade simulations.

Experimental studies involving cerebellar lesions have been important tools for learning about the operation of the saccadic eye movement system. These studies have been used to further develop a neural network model for horizontal saccadic eye movement control. The neural control mechanism is first order time optimal, initiated by the deep layers of the superior colliculus and terminated by the cerebellar fastigial nucleus. The neural circuit consists of neurons in the paramedian pontine reticular formation (burst, tonic and pause cells), the vestibular nucleus, abducens nucleus, oculomotor nucleus, cerebellum, substantia nigra, nucleus reticularis tegmenti pontis, the thalamus, the deep layers of the superior colliculus and the oculomotor plant for each eye. Agonist burst cell activity is initiated with maximal firing due to an error between the target and eye position, and continues until the internal eye position in the cerebellar vermis reaches the desired position, then decays to zero. The cerebellar vermis is responsible for adapting the duration of maximal firing based on the initial position of the eye. There are two sets of neural integrators in the neural network. One operates within the cerebellar vermis to predict the width of the pulse, and the other within the paramedian pontine reticular formation to maintain the eyes at their destination. Antagonist neural activity is inhibited during the agonist burst activity. After the agonist burst, antagonist neural activity rises with a stochastic rebound burst and from input from the fastigial nucleus, then falls to a tonic firing level necessary to keep the eye at its destination. The onset of the antagonist tonic firing is stochastic, weakly coordinated with the end of the agonist burst, and under cerebellar control. A common mechanism of action is described, based on cerebellar gating, through the fastigial nucleus, that explains a number of different saccadic eye movement types, including dynamic overshoot, glissadic overshoot and undershoot, and undershoot. A linear homeomorphic oculomotor muscle model is used in the simulations of the operation of the neural network. Each of the neural sites in the model fire similar to experimental data, and simulate fast eye movements.

Cerebellum↗

PubMed1996

Application of robust data processing methods to the analysis of eye movements.

We have developed a robust, nonlinear differentiating digital filter for the estimation of eye velocity from an eye-position signal. This filter is equally applicable to a large variety of other biomedical signals. The filter is implemented by taking an odd number of two-point differences around the point of interest and then selecting the median difference. Dividing the median difference by the time interval yields the derivative of the input signal. The Robust Differentiator (RD) is a classic order-statistic filter consisting of a bank of linear filters (the two-point differences) followed by the median operation to select one difference for the derivative calculation. The RD has no impulse response function and eliminates the "ringing" that is typical of all linear filters. The RD not only performs better than the FIR differentiating filters for impulse noise but is equally effective for both broad-band and narrow-band Gaussian noise. The RD "bandwidth" is adjustable by selecting the interval for the two-point differences.

Eye Movements↗

PubMed1995

Eye tracking performance variability in a homogeneous population.

Pursuit tracking eye movements were recorded and analyzed from a group of US Air Force Pilot Candidates (PCs). The PCs ranged in age from 21 to 27 with a median age of 23. All were college graduates and recently passed a Flying Class I physical exam. These PCs comprise a highly motivated, intelligent group of young subjects. Pursuit tracking was assessed by having the subjects track a small spot of green light moving sinusoidally in the horizontal plane at frequencies from 0.2 to 1.0 Hz in 0.2 Hz increments. Peak-to-peak target amplitude was 40 degrees. Eye movements were recorded using an infrared reflectance device. Eye movements were separated into smooth pursuit (SP) and saccadic (SA) components. Tracking performance was evaluated by computing the gain and asymmetry of the SP component and the percentage of tracking movements contributed by the SA component. Both mean values and variance of the tracking performance of the PCs were not found to be statistically different from a group consisting of both flying and nonflying Air Force personnel.

Adult↗

PubMed1995

Simulation of oculomotor post-inhibitory rebound burst firing using a Hodgkin-Huxley model of a neuron.

A number of theories have been reported on post saccade phenomenon describing dynamic overshoot, glissadic overshoot and undershoot, and undershoot, all naturally and frequently occurring saccadic eye movements. Electrophysiological evidence for post-inhibitory rebound burst firing activity during saccadic eye movements is prevalent in the literature. However, the cause for the phenomenon is not known. Marked inhibition of neurons within the Paramedian Pontine Reticular Formation often results in post-inhibitory rebound burst firing activity at the beginning and end of a saccade. In this paper, post-inhibitory rebound burst firing activity after marked hyperpolarization is postulated to occur in the Paramedian Pontine Reticular Formation due to a low membrane threshold voltage. With this biophysical property, a single neuron is capable of firing at high rates automatically and without stimulation when released from inhibition. Simulations using the Hodgkin-Huxley model of a neuron demonstrate that a single neuron is capable of firing at high rates automatically without stimulation when released from inhibition.

Electrophysiology↗

PubMed1994

Analysis of pursuit tracking eye movements in pilots and nonfliers.

Pursuit tracking eye movements were recorded and analyzed from a group of Air Force pilots and a group of nonflying Air Force members. The tracking performance of the pilots was compared to the performance of the nonfliers. Subjects tracked a small spot of light moving sinusoidally in the horizontal plane at frequencies ranging from 0.2 to 1.0 Hz at a peak-to-peak amplitude of 40 degrees. Maximum target velocities ranged from 25 to 126 degrees/s. An adaptive nonlinear digital filter was used to separate the total tracking response (TTR) into smooth pursuit (SP) and saccadic (SA) components. Frequency domain analysis was used to relate the tracking components to the target movement. There were no statistically significant differences in tracking performance between the pilots and nonfliers. When tracking the 0.2 and 0.4 Hz targets, the TTR consisted principally of SP tracking with SA tracking representing less than 11% of the TTR. As the target velocity increased, the TTR remained adequate but the proportion of SP tracking decreased while the SA tracking increased. Over one-half of the TTR is contributed by the SA system when tracking the 1.0 Hz targets for both the pilot and nonpilot groups.

Adult↗

PubMed1994

The application of smooth pursuit eye movement analysis to clinical medicine.

Pursuit tracking eye movements were analyzed from selected patients with neurological injuries and compared to the responses of 20 normal subjects. The patients/subjects tracked a small spot of light moving sinusoidally in the horizontal plane at a frequency of 0.4 Hz and a peak-to-peak amplitude of 40 degrees. Eye-movement responses were separated into a smooth-pursuit component and a saccadic component. The smooth-pursuit component was analyzed by calculating the gain, phase, and asymmetry. The saccadic component was quantified by calculating the percentage of the total tracking movement contributed by the saccadic system. The patients with smooth-pursuit impairment exhibited a higher percentage of saccadic tracking and a lower smooth pursuit gain compared to the normal subjects. One patient with a unilateral lesion exhibited significant asymmetry in the smooth-pursuit component. In this case, the direction of the asymmetry indicated the side of the lesion.

Adult↗

PubMed1993

Linear systems analysis of the vestibulo-ocular reflex: clinical applications.

We used whole-body angular acceleration stimuli to estimate the transfer function of the vestibuloocular reflex in 20 normal subjects and several patients. Eye movements evoked by the stimuli were recorded and an adaptive nonlinear digital filter was used to extract the compensatory component of the eye-movement response. Frequency domain analysis of the stimulus and the compensatory component of the response was used to estimate the transfer function. The transfer function was estimated at 6 discrete frequencies (0.01 to 0.32 Hz by octaves) using individual sine-wave stimuli. Data from several patients with known lesions affecting the vestibular system were compared to the normal responses. Patients with a unilateral vestibular loss demonstrated low gains at frequencies below 0.02 Hz and large phase shifts below 0.32 Hz, but had normal responses at 0.32 Hz. Some of these same patients also exhibited a significant directional asymmetry in their responses.

Adult↗

PubMed1992

Selecting a stimulus signal for linear systems analysis of the vestibulo-ocular reflex.

We evaluated 3 types of stimulus signals for use in estimating the transfer function of the vestibulo-ocular reflex. We used individual sine-wave, sum-of-sine, and pseudorandom stimuli. Five normal human subjects were tested 5 times each using each of the 3 stimulus types. Frequency domain techniques were used to estimate the transfer function at 0.01, 0.03, and 0.05 Hz. The most consistent estimates were obtained using individual sine-wave stimuli. The pseudorandom signal yielded the most variable estimates. A sum-of-sine stimulus composed of 3 sine-wave signals provided estimates slightly more variable than the individual sine-wave stimuli, but much less variable than the pseudorandom stimulus. The redundency of the sine and sum-of-sine stimuli seems to be an advantage by providing stable estimates of the transfer function in the presence of noise.

Adult↗

PubMed1991

A comparison of static and dynamic characteristics between rectus eye muscle and linear muscle model predictions.

The characteristics of a muscle model are analyzed using rectus eye muscle parameter values and compared to rectus eye muscle data. The muscle is modeled as a viscoelastic parallel combination connected to a parallel combination of active state tension generator, viscosity element, and length tension elastic element. Each of the elements is linear and their existence is supported with physiological evidence. The static and dynamic properties of the muscle model are compared to rectus eye muscle data. The length-tension characteristics of the model are in good agreement with the data within the operating region of the muscle. With the muscle model incorporated into a lever system to match the isotonic experiment paradigm, simulation results for this linear system yield a nonlinear force-velocity curve. Moreover, the family of force-velocity curves generated with different stimulus rates reported in the literature match the predictions of the model without parametric changes. The results of this paper are important in studies involving the oculomotor plant and oculomotor neural networks. Additionally, these results may be applicable to other muscles.

Isometric Contraction↗

PubMed1991

Relationships between manual reaction time and saccade latency in response to visual and auditory stimuli.

Manual reaction time (RT) responses were analyzed from seven human subjects. Responses were recorded using four kinds of target presentations: fixed visual target, moving visual target, fixed auditory target, and moving auditory target. Moving targets (moving in the horizontal plane) were presented at constant intensity and provided only a motion cue. Fixed targets "popped on" at the primary position and provided only an onset cue. RTs for the fixed and moving visual targets were 241.5 ms and 233.1 ms, respectively. The 8.4 ms (3.5%) advantage for the moving visual target over the fixed visual target was statistically significant, p less than 0.05. RT for the moving auditory target varied with target movement amplitude and ranged from 219 ms for 40 degree movements to 268 ms for 5 degree movements. For the fixed auditory target in the sagittal plane, average RT was 182.9 ms. Thus, sound-source motion detection was from 36 to 85 ms slower than sound onset detection, p less than 0.001. The RT results were compared to saccade latency measurements from an earlier study. Both RT and saccade latency showed the same dependency upon target movement amplitude. For small target displacements, saccade latencies for the moving auditory target were longer than for the moving visual target. The longer latencies for the moving auditory target are attributed to the increased processing time required to detect the sound-source motion.

Acoustic Stimulation↗

PubMed1991

Digitally calibrated eye movement measurement system.

A less expensive instrumentation alternative to record horizontal eye movements is presented in the form of a feasibility study. Currently, there are several techniques used to accurately measure eye position for research purposes. To implement these techniques, significant financial resources are required. The proposed technique costs less than $3000.00 and is based upon the limbus position technique. A linear charged coupled device (CCD) image array sensor and a biconvex lens are used to determine the position of the pupil. The self scanned CCD array is controlled by four analog clock signals generated from digital logic. The CCD video output signal is converted to a TTL compatible signal. The TTL signal is used to create a digital number based upon the limbus image formation upon the CCD pixels. This digital number is processed using standard signal acquisition techniques and is recorded on the hard drive disk of an IBM PC/AT compatible system. The C programming language is used to acquire the data and control calibration of the system. FORTRAN software is used to implement digital signal processing algorithms and estimate eye position.

Calibration↗

PubMed1991

Saccades simulated with rhesus monkey innervation data.

Extracellular single-unit data from the vicinity of the abducens nucleus from rhesus monkeys recorded during horizontal saccades are used as input to an updated oculomotor plant to simulate saccades to test the robustness of the model. Cells recorded from include: Long Lead Burst neurons, Medium Lead Burst neurons, and Burst Tonic neurons. Eye movement position data was collected using the magnetic coil technique. The oculomotor plant used in this study consists of the lateral and medial rectus muscle, and the eyeball. The muscles are modeled as a viscoelastic parallel combination connected to a parallel combination of active state tension generator, viscosity element and length tension elastic element. The eyeball is modeled as a sphere, connected to two parallel viscoelastic elements, connected in series. Each of the elements is linear. All parameters and initial conditions are estimated or directly measured from physiological data. The active state tension generator waveforms are the low-passed filtered motoneural signals. The extracellular single-unit data is used to drive the agonist portion of a neural circuit, consisting of burst, tonic and pause cells. The antagonist portion of the neural activity is based on previously reported characteristics. Simulation results for eye position and higher derivatives are in good agreement with the eye position data and the data derived estimates of higher derivatives.

Animals↗

PubMed1991

Optimization of an adaptive nonlinear filter for the analysis of nystagmus.

An adaptive nonlinear digital filter has been designed for the analysis of an eye-movement signal called nystagmus. Nystagmus is a bi-phasic signal consisting of a sequence of tracking eye movements called "slow-phase" interspersed with brief, high-velocity refixation movements called "fast-phase." The objective of the analysis is to separate the nystagmus signal into its fast- and slow-phase components. Specifically, the goal is to produce an evenly sampled estimate of slow-phase velocity (SPV) and an estimate of the peak fast-phase velocity. Classically this has been done using pattern recognition methods that exploit the fact that the fast-phase is a relatively short duration, high-velocity movement compared to the slow-phase. Unfortunately, these velocity and duration differences do not reliably separate the slow- and fast-phases under all conditions, especially when the signal is noisy. We have designed and built an adaptive nonlinear digital filter that easily outperforms the more complex pattern recognition algorithms. This new filter, called an Adaptive Asymmetrically Trimmed-Mean (AATM) filter, works under the assumption that, on the average, the eyes spend more time in slow-phase than in fast-phase. Thus, in any given data segment, most of the data samples are slow-phase samples. By analyzing the amplitude distribution of the data samples in the segment we can determine which of these samples are slow-phase. We used computer generated nystagmus signals contaminated with 3 levels of noise to evaluate the filter. The filter parameters were then optimized using Monte Carlo procedures producing an extremely robust analysis method.

Electronystagmography↗

PubMed1990

Periodic saccadic oscillations and tinnitus.

A man with essential hypertension developed stereotyped cycles of oscillopsia and bilateral "sparking" tinnitus. Eye movement recordings showed cycles of disconjugate opsoclonus, square-wave jerks, and saccadic dynamic overshoot disrupting stable fixation. Neuroimaging studies were normal. We postulate a lesion episodically disturbing saccade-related neurons and central auditory neurons in the pons.

Eye Movements↗

PubMed1990

A new approach to the analysis of nystagmus: an application for order-statistic filters.

A computer program has been designed for the analysis of nystagmus. This program employs a class of nonlinear digital filters called order-statistic (OS) filters. Two OS filters and one linear filter are used. First, the eye-movement signal is smoothed using a predictive finite-impulse response (FIR), median hybrid filter. Then the smoothed signal is processed by a linear band-limited differentiating filter to calculate eye velocity. And finally, the slow-phase velocity (SPV) envelope is extracted from the eye-velocity signal using an adaptive asymmetrically trimmed-mean filter. This approach yields an evenly sampled SPV estimate without resorting to the various interpolation or extrapolation schemes generally used. The adaptive filter estimates SPV based on the local statistical properties of the eye-velocity signal. The adaptive strategy works under the assumption that, on the average, the eyes spend more time in slow-phase than in fast-phase. No assumptions are made about the direction of the nystagmus or the nature of the stimulus used to elicit the nystagmus. This method eliminates all the usual threshold tests and decision logic common to other nystagmus analysis programs. The robust performance of OS filters and the use of adaptive filter structures totally eliminates the need to custom "tune" the program parameters for atypical data sets.

Aircraft↗

PubMed1990

Development of a non-linear smoothing filter for the processing of eye-movement signals.

The analysis of eye-movement (EM) signals poses problems for the designer of smoothing filters since many of the interesting types of EMs are bimodal. For example, optokinetic and/or vestibular stimulation results in an EM pattern called nystagmus consisting of alternating fast- and slow-phase components. Also, saccadic (refixation) EMs do not occur continuously, but are interspersed with periods of fixation. Conventional linear, low-pass filters (both finite impulse response (FIR) and infinite impulse response (IIR) types) smear the boundries between the fast- and slow-phases of nystagmus and the fixation and fast components of saccadic EMs. We have adapted a nonlinear smoothing filter (originally designed to optimize edge preservation in image processing applications) for the smoothing of EM signals. This filter is called a Predictive FIR-Median Hybrid (PFMH) filter. The PFMH filter operates on a moving window of data samples centered at the current point of interest. Several predictive FIR filters are applied to the "upper" and "lower" halves of the window and each are designed to predict the sample value at the center of the window. The median of these FIR filter outputs and the actual center data sample are taken as the PFMH filter output for each window position. By properly choosing the length and structure of the FIR subfilters, a PFMH filter can be designed to smooth a bimodal EM signal without blurring the boundries between the two signal components.

Electrooculography↗

PubMed1990

Additional developments in oculomotor plant modeling.

A new oculomotor plant is presented in this study using an updated third-order linear muscle model. The lateral and medial rectus muscle is modeled as a viscoelastic parallel combination connected to a parallel combination of active state tension generator, viscosity element and length tension elastic element. The eyeball is modeled as a sphere, connected to two parallel viscoelastic elements, connected in series. Each of the elements is linear. The static and dynamic properties of the muscle model are in good agreement with rectus muscle data. The length-tension characteristics of the model match the data within the operating region of the muscle. Simulation results for the muscle model yield hyperbolic shaped force-velocity curves that match the data very well. All parameters and initial conditions are estimated or directly measured from physiological data. The oculomotor plant is derived through direct programming state-space representation by Laplace variable analysis about the operating point or initial eye position. The form of the oculomotor plant makes this representation even more ideal than previous models for use in the development of more sensitive tests of oculomotor pathology and in the description of normal oculomotor function.

Elasticity↗